What the documents look like
Store count and loss-control retrieval draws on POS inventory, WMS, monitoring systems, and count standards. Day-to-day documents are count sheets, variance analyses, loss-report approvals, and monitoring access records. These materials update monthly and quarterly. In cross-border settings, the same count materials may exist in Chinese and in foreign-language versions, so retrieval needs cross-language semantic matching.
A count sheet records SKU, book quantity, physical quantity, and variance. A variance analysis states the cause and the follow-up conclusion. A loss-report approval records the application, the approval, and the execution status. A monitoring access record holds access time, scope, and purpose. These fields mean retrieval must distinguish states such as "submitted," "approved," and "executed." Vector similarity on its own does not carry that distinction.
Constraints at the vector indexes step
Knowledge bases associated for one retrieval must use the same vector model. Check the actual model and the input type first. One source data block can link to several indexes, and index content and source content are checked separately. Indexes are best built from complete and focused information, and retrieval coverage against index length is weighed with samples. Image retrieval also depends on whether image descriptions or image vectors were generated at ingestion time, so the query side alone does not decide it. After a model or index change, compare cited source text with the same set of questions; do not promise an outcome from a vector score alone.
Semantic retrieval computes similarity from vector distance and supports cross-language understanding, for example a Chinese question matching a foreign-language knowledge point. Full-text retrieval suits exact queries such as numbers, models, and proper nouns. Hybrid retrieval uses both and merges them through RRF. RRF is a result-fusion process and does not create permission or entity isolation. Minimum relevance is a filter threshold that applies only in semantic retrieval or when result reranking is used, and it does not exclude unrelated businesses or unauthorized records. The citation limit controls the amount of cited content in tokens. Retrieval itself is not authorization; whether a loss report is approved or a monitoring access request is granted needs downstream system or human confirmation.
Hypothetical paper exercise
The source text to be checked reads: "The March 2024 count sheet shows SKU-A book quantity 120 units and physical quantity 108 units. The variance analysis records the cause of this variance as 'to be verified.' The loss-report approval form records the SKU-A loss-report application status as 'submitted,' and the approval status field is empty."
One draft sentence to be judged reads: "The SKU-A loss report has been approved." Compare the draft sentence with the approval status field on the approval form. The approval status field is empty, so the draft sentence is not supported by the materials.
Configuration and implementation checks
| Configuration item | Assumed trial starting point | Reason and comparison object |
|---|---|---|
| Search mode | Hybrid retrieval | Count sheets carry SKU numbers and natural-language variance descriptions, so exact numbers and semantic matching are both needed |
| Vector model | Same as the associated knowledge base | Check the actual model identifier first and exclude bases with a different vector model |
| Index content | Keep the full variance description per block | Compare recall coverage with an index that keeps only the SKU number |
| Minimum relevance | Assumed starting point 0.3 | Compare with 0.5 and observe whether cited content includes unrelated fragments |
| Citation limit | Assumed starting point 2000 tokens | Compare with 1000 tokens and observe whether cited content covers the full variance description |
| Result reranking | Enabled | Compare with disabled and observe whether ordering tracks the question more closely when candidates are numerous |
These values are trial starting points for this exercise, used to compare the same set of questions. They are not official defaults.
Three common errors
- Reading an empty approval status field as "approved." The error is treating an unstated status as a positive conclusion. Check action: input the loss-report approval form source text, compare the value of the approval status field, and observe that an empty field is recorded as unsupported.
- Treating minimum relevance as a permission filter. The error is assuming a lower threshold recalls unauthorized records. Check action: input the same query, compare cited content under different thresholds, and observe whether cited content still comes from the same knowledge base scope; permission judgment needs a separate system implementation.
- Associating knowledge bases with different vector models. The error is ignoring the same-model requirement. Check action: input the model identifiers of two knowledge bases, compare whether they match, and observe that a mismatch means the non-matching base is excluded and no cross-model retrieval comparison is run.
How to verify the setup
- Verification one: input the loss-report approval form source text from the hypothetical exercise and the draft sentence "The SKU-A loss report has been approved," compare the value of the approval status field, and observe that an empty field is judged unsupported.
- Verification two: input the query "SKU-A variance," compare the cited content returned by hybrid retrieval and by semantic retrieval, and observe whether hybrid retrieval covers both the SKU number and the variance description.
- Verification three: input the same set of questions, compare the cited source text before and after an index content change, and observe whether cited content is more focused on the variance description, without judging the effect from a vector score alone.
Empty-result handling can use an explicit paper input of [] to check the downstream design, without claiming that a fictional rule retrieval always returns []. Image retrieval requires a corresponding image input and model capability, and reranking requires a model and must be enabled by setting. Knowledge bases with different vector models cannot be associated for one retrieval; check identifiers only, exclude non-matching bases, and record what is actually observed.